语体变异是语码转换的优势
Style Variation as a Vantage Point for Code-Switching
摘要：
语码转换（CS）是在一些双语和多语言社区中观察到的一种常见现象，从而在数字和社交媒体平台上流行。这种日益突出的需求需要为关键的下游任务建模CS语言。这个领域的一个主要问题是缺乏注释数据和大量的语料库来训练大规模的神经模型。生成大量高质量的文本有助于严重依赖语言建模的几个下游任务，例如语音识别、文本到语音合成等。我们提出了一个新的有利点CS的风格变化之间的参与语言。我们的方法不需要任何外部注释，如词法语言ID。它主要依赖于容易获得的单语语料库，没有任何平行对齐和有限的自然CS句子集。我们提出了一个两阶段的生成对抗训练方法，其中第一阶段生成CS的竞争性否定示例，第二阶段生成更真实的CS句子。我们提出了我们的实验对以下几种语言：西班牙语英语，普通话英语，印度语英语和阿拉伯语法语。我们发现，通过双阶段训练过程，生成的CS的指标趋势更接近上述每种语言对中的真实CS数据。我们相信，CS作为风格变化的这种观点为CS文本中的各种任务建模开辟了新的视角。
英文摘要：
Code-Switching (CS) is a common phenomenon observed in several bilingual and multilingual communities, thereby attaining prevalence in digital and social media platforms. This increasing prominence demands the need to model CS languages for critical downstream tasks. A major problem in this domain is the dearth of annotated data and a substantial corpora to train large scale neural models. Generating vast amounts of quality text assists several down stream tasks that heavily rely on language modeling such as speech recognition, text-to-speech synthesis etc,. We present a novel vantage point of CS to be style variations between both the participating languages. Our approach does not need any external annotations such as lexical language ids. It mainly relies on easily obtainable monolingual corpora without any parallel alignment and a limited set of naturally CS sentences. We propose a two-stage generative adversarial training approach where the first stage generates competitive negative examples for CS and the second stage generates more realistic CS sentences. We present our experiments on the following pairs of languages: Spanish-English, Mandarin-English, Hindi-English and Arabic-French. We show that the trends in metrics for generated CS move closer to real CS data in each of the above language pairs through the dual stage training process. We believe this viewpoint of CS as style variations opens new perspectives for modeling various tasks in CS text.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
